{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年4月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer Salary in First-Tier Cities 程序员工资调查\n",
    "我在4月1日到3日之间，抓取了某招聘网站的软件和互联网类招聘数据40万条，其中通过程序判断为程序员的14万条。地域方面，我选择了24个主要城市。不过本文只以一线城市为研究对象。这样是为了和我2017年6月的数据做对比。\n",
    "\n",
    "提到2017年的文章，现在居然还有很多人，把这篇文章拿出来炒作。对于社会来说，可气的是，他们直接把2017改成2019，就发表了，这不是骗人么？！对于我来说，可气的是，他们转载居然还冒充是原创，是可忍熟不可忍！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "sys.path.append('../py')\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import weighted\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and city in ('北京','上海','广州','深圳')  and monthly_salary<80000\", con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del data['publish_date']\n",
    "#del data['published_on_weekend']\n",
    "#del data['title']\n",
    "#del data['title']\n",
    "#del data['company_title']\n",
    "#del data['company_description']\n",
    "#del data['job_description']\n",
    "#del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_id</th>\n",
       "      <th>monthly_salary</th>\n",
       "      <th>headcount</th>\n",
       "      <th>title</th>\n",
       "      <th>zhinengleibie</th>\n",
       "      <th>career</th>\n",
       "      <th>real_province</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>company_id</th>\n",
       "      <th>...</th>\n",
       "      <th>bd_hbase</th>\n",
       "      <th>bd_storm</th>\n",
       "      <th>bd_pig</th>\n",
       "      <th>bd_mahout</th>\n",
       "      <th>bd_impala</th>\n",
       "      <th>bd_yarn</th>\n",
       "      <th>bd_alluxio</th>\n",
       "      <th>bd_flink</th>\n",
       "      <th>bd_presto</th>\n",
       "      <th>bd_heron</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57944</th>\n",
       "      <td>99101707</td>\n",
       "      <td>10000</td>\n",
       "      <td>500</td>\n",
       "      <td>高薪诚聘专车司机</td>\n",
       "      <td>推荐算法工程师</td>\n",
       "      <td>推荐算法工程师</td>\n",
       "      <td>None</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>co4768735</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48720</th>\n",
       "      <td>120903677</td>\n",
       "      <td>12500</td>\n",
       "      <td>460</td>\n",
       "      <td>web前端开发</td>\n",
       "      <td>前端开发</td>\n",
       "      <td>前端开发</td>\n",
       "      <td>None</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>co5810766</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42221</th>\n",
       "      <td>120372068</td>\n",
       "      <td>9000</td>\n",
       "      <td>280</td>\n",
       "      <td>软件开发、数据分析、产品、设计类岗位</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>None</td>\n",
       "      <td>异地招聘</td>\n",
       "      <td>上海</td>\n",
       "      <td>co5782817</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42220</th>\n",
       "      <td>120372004</td>\n",
       "      <td>9000</td>\n",
       "      <td>280</td>\n",
       "      <td>软件开发、数据分析、产品、设计类岗位</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>None</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>co5782817</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19253</th>\n",
       "      <td>116717167</td>\n",
       "      <td>10000</td>\n",
       "      <td>200</td>\n",
       "      <td>海外业务软件工程师（2020届）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>None</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>co618738</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 114 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          job_id  monthly_salary  headcount               title zhinengleibie  \\\n",
       "57944   99101707           10000        500            高薪诚聘专车司机       推荐算法工程师   \n",
       "48720  120903677           12500        460             web前端开发          前端开发   \n",
       "42221  120372068            9000        280  软件开发、数据分析、产品、设计类岗位         软件工程师   \n",
       "42220  120372004            9000        280  软件开发、数据分析、产品、设计类岗位         软件工程师   \n",
       "19253  116717167           10000        200    海外业务软件工程师（2020届）         软件工程师   \n",
       "\n",
       "        career real_province province city company_id  ... bd_hbase bd_storm  \\\n",
       "57944  推荐算法工程师          None       上海   上海  co4768735  ...    False    False   \n",
       "48720     前端开发          None       广东   深圳  co5810766  ...    False    False   \n",
       "42221    软件工程师          None     异地招聘   上海  co5782817  ...    False    False   \n",
       "42220    软件工程师          None       上海   上海  co5782817  ...    False    False   \n",
       "19253    软件工程师          None       上海   上海   co618738  ...    False    False   \n",
       "\n",
       "       bd_pig bd_mahout  bd_impala  bd_yarn bd_alluxio bd_flink bd_presto  \\\n",
       "57944   False     False      False    False      False    False     False   \n",
       "48720   False     False      False    False      False    False     False   \n",
       "42221   False     False      False    False      False    False     False   \n",
       "42220   False     False      False    False      False    False     False   \n",
       "19253   False     False      False    False      False    False     False   \n",
       "\n",
       "      bd_heron  \n",
       "57944    False  \n",
       "48720    False  \n",
       "42221    False  \n",
       "42220    False  \n",
       "19253    False  \n",
       "\n",
       "[5 rows x 114 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.sort_values(by='headcount', ascending=False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_weighted_mean(group, avg_name, weight_name):\n",
    "    \"\"\" http://stackoverflow.com/questions/10951341/pandas-dataframe-aggregate-function-using-multiple-columns\n",
    "    In rare instance, we may not have weights, so just return the mean. Customize this if your business case\n",
    "    should return otherwise.\n",
    "    \"\"\"\n",
    "    d = group[avg_name]\n",
    "    w = group[weight_name]\n",
    "    try:\n",
    "        return (d * w).sum() / w.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return d.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58340, 114)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "191275"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5500., 11500., 14583., 20000., 37500.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_mean=weighted.weighted_mean(data.monthly_salary.values, data.headcount.values)\n",
    "q=weighted.weighted_quantile(data.monthly_salary.values,[0.025,0.25,0.5,0.75,0.975], data.headcount.values)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年4月中国一线城市程序员的平均工资为16438元，工资中位数为14583元，其中95%的人的工资位于5500到37500元之间。\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月中国一线城市程序员的平均工资为{salary_mean:.0f}元，工资中位数为{q[2]:.0f}元，其中95%的人的工资位于{q[0]:.0f}到{q[4]:.0f}元之间。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2020, Developers in China earn 16438 Yuan as average, the median is 14583 Yuan, 95% of them earn between 5500 and 37500 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print(f'In {year}, Developers in China earn {salary_mean:.0f} Yuan as average, '\n",
    "      f'the median is {q[2]:.0f} Yuan, 95% of them earn between {q[0]:.0f} and {q[4]:.0f} Yuan.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=18721.323749298317, pvalue=0.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(data.monthly_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "null hypothesis: x comes from a normal distribution\n",
    "    \n",
    "p=0\n",
    "\n",
    "The null hypothesis can be rejected\n",
    "\n",
    "conclusion: data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data.monthly_salary<40000].monthly_salary.hist()\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0,0), xytext=(2000, 100), color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Common Functions\n",
    "def get_sub_stats_by_col(data, col):\n",
    "    categories=data[col].unique()\n",
    "    salary_mean=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    salary_median=[]\n",
    "    \n",
    "\n",
    "    count=[]\n",
    "    \n",
    "    categorys_out=[]\n",
    "    for category in categories:\n",
    "        #print(feature)\n",
    "        idata=data[data[col]==category]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(np.average(values, weights=weights))\n",
    "        \n",
    "\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        categorys_out.append(category)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[col]=[c for c in categorys_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data\n",
    "\n",
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "    {\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031ef\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >career</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >46</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow0_col0\" class=\"data row0 col0\" >CTO</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow0_col1\" class=\"data row0 col1\" >39288</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow0_col2\" class=\"data row0 col2\" >25000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow0_col3\" class=\"data row0 col3\" >36250</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow0_col4\" class=\"data row0 col4\" >69041.600000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow0_col5\" class=\"data row0 col5\" >24</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >39</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow1_col0\" class=\"data row1 col0\" >编译器开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow1_col1\" class=\"data row1 col1\" >30697</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow1_col2\" class=\"data row1 col2\" >12500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow1_col3\" class=\"data row1 col3\" >32500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow1_col4\" class=\"data row1 col4\" >39604.166667</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow1_col5\" class=\"data row1 col5\" >39</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow1_col6\" class=\"data row1 col6\" >0.02%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >14</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow2_col0\" class=\"data row2 col0\" >架构师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow2_col1\" class=\"data row2 col1\" >28198</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow2_col2\" class=\"data row2 col2\" >11500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow2_col3\" class=\"data row2 col3\" >27500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow2_col4\" class=\"data row2 col4\" >54270.250000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow2_col5\" class=\"data row2 col5\" >3615</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow2_col6\" class=\"data row2 col6\" >1.89%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >12</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow3_col0\" class=\"data row3 col0\" >系统架构设计师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow3_col1\" class=\"data row3 col1\" >27350</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow3_col2\" class=\"data row3 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow3_col3\" class=\"data row3 col3\" >28666</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow3_col4\" class=\"data row3 col4\" >45833.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow3_col5\" class=\"data row3 col5\" >1273</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow3_col6\" class=\"data row3 col6\" >0.67%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >48</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow4_col0\" class=\"data row4 col0\" >SLAM</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow4_col1\" class=\"data row4 col1\" >26231</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow4_col2\" class=\"data row4 col2\" >12500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow4_col3\" class=\"data row4 col3\" >30000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow4_col4\" class=\"data row4 col4\" >30000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow4_col5\" class=\"data row4 col5\" >13</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row5\" class=\"row_heading level0 row5\" >36</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow5_col0\" class=\"data row5 col0\" >芯片</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow5_col1\" class=\"data row5 col1\" >25508</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow5_col2\" class=\"data row5 col2\" >10495.454545</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow5_col3\" class=\"data row5 col3\" >22500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow5_col4\" class=\"data row5 col4\" >50000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow5_col5\" class=\"data row5 col5\" >114</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.06%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row6\" class=\"row_heading level0 row6\" >42</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow6_col0\" class=\"data row6 col0\" >ADAS</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow6_col1\" class=\"data row6 col1\" >24086</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow6_col2\" class=\"data row6 col2\" >10416.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow6_col3\" class=\"data row6 col3\" >26667</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow6_col4\" class=\"data row6 col4\" >45291.350000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow6_col5\" class=\"data row6 col5\" >26</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow6_col6\" class=\"data row6 col6\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row7\" class=\"row_heading level0 row7\" >22</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow7_col0\" class=\"data row7 col0\" >机器学习</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow7_col1\" class=\"data row7 col1\" >23818</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow7_col2\" class=\"data row7 col2\" >5250.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow7_col3\" class=\"data row7 col3\" >22500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow7_col4\" class=\"data row7 col4\" >58333.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow7_col5\" class=\"data row7 col5\" >825</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow7_col6\" class=\"data row7 col6\" >0.43%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row8\" class=\"row_heading level0 row8\" >5</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow8_col0\" class=\"data row8 col0\" >算法工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow8_col1\" class=\"data row8 col1\" >22302</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow8_col2\" class=\"data row8 col2\" >7500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow8_col3\" class=\"data row8 col3\" >20833</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow8_col4\" class=\"data row8 col4\" >45000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow8_col5\" class=\"data row8 col5\" >6564</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow8_col6\" class=\"data row8 col6\" >3.43%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row9\" class=\"row_heading level0 row9\" >26</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow9_col0\" class=\"data row9 col0\" >图像算法工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow9_col1\" class=\"data row9 col1\" >22119</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow9_col2\" class=\"data row9 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow9_col3\" class=\"data row9 col3\" >20000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow9_col4\" class=\"data row9 col4\" >45000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow9_col5\" class=\"data row9 col5\" >1174</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow9_col6\" class=\"data row9 col6\" >0.61%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row10\" class=\"row_heading level0 row10\" >37</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow10_col0\" class=\"data row10 col0\" >FPGA</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow10_col1\" class=\"data row10 col1\" >21306</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow10_col2\" class=\"data row10 col2\" >10225.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow10_col3\" class=\"data row10 col3\" >20000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow10_col4\" class=\"data row10 col4\" >35458.150000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow10_col5\" class=\"data row10 col5\" >238</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow10_col6\" class=\"data row10 col6\" >0.12%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row11\" class=\"row_heading level0 row11\" >41</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow11_col0\" class=\"data row11 col0\" >DSP</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow11_col1\" class=\"data row11 col1\" >20558</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow11_col2\" class=\"data row11 col2\" >5683.928571</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow11_col3\" class=\"data row11 col3\" >20000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow11_col4\" class=\"data row11 col4\" >49020.755000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow11_col5\" class=\"data row11 col5\" >147</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow11_col6\" class=\"data row11 col6\" >0.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row12\" class=\"row_heading level0 row12\" >11</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow12_col0\" class=\"data row12 col0\" >区块链开发</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow12_col1\" class=\"data row12 col1\" >20442</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow12_col2\" class=\"data row12 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow12_col3\" class=\"data row12 col3\" >17500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow12_col4\" class=\"data row12 col4\" >55500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow12_col5\" class=\"data row12 col5\" >572</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow12_col6\" class=\"data row12 col6\" >0.30%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row13\" class=\"row_heading level0 row13\" >17</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow13_col0\" class=\"data row13 col0\" >Hadoop工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow13_col1\" class=\"data row13 col1\" >20124</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow13_col2\" class=\"data row13 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow13_col3\" class=\"data row13 col3\" >17500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow13_col4\" class=\"data row13 col4\" >41516.666667</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow13_col5\" class=\"data row13 col5\" >1569</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow13_col6\" class=\"data row13 col6\" >0.82%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row14\" class=\"row_heading level0 row14\" >9</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow14_col0\" class=\"data row14 col0\" >大数据</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow14_col1\" class=\"data row14 col1\" >20001</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow14_col2\" class=\"data row14 col2\" >7500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow14_col3\" class=\"data row14 col3\" >17500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow14_col4\" class=\"data row14 col4\" >43812.500000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow14_col5\" class=\"data row14 col5\" >3759</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow14_col6\" class=\"data row14 col6\" >1.97%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row15\" class=\"row_heading level0 row15\" >47</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow15_col0\" class=\"data row15 col0\" >信号处理</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow15_col1\" class=\"data row15 col1\" >18971</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow15_col2\" class=\"data row15 col2\" >12500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow15_col3\" class=\"data row15 col3\" >20833</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow15_col4\" class=\"data row15 col4\" >22500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow15_col5\" class=\"data row15 col5\" >17</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow15_col6\" class=\"data row15 col6\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row16\" class=\"row_heading level0 row16\" >3</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow16_col0\" class=\"data row16 col0\" >移动开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow16_col1\" class=\"data row16 col1\" >18764</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow16_col2\" class=\"data row16 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow16_col3\" class=\"data row16 col3\" >16000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow16_col4\" class=\"data row16 col4\" >39350.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow16_col5\" class=\"data row16 col5\" >2938</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow16_col6\" class=\"data row16 col6\" >1.54%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row17\" class=\"row_heading level0 row17\" >13</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow17_col0\" class=\"data row17 col0\" >Unity3d开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow17_col1\" class=\"data row17 col1\" >18146</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow17_col2\" class=\"data row17 col2\" >7108.333333</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow17_col3\" class=\"data row17 col3\" >15000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow17_col4\" class=\"data row17 col4\" >41125.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow17_col5\" class=\"data row17 col5\" >486</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow17_col6\" class=\"data row17 col6\" >0.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row18\" class=\"row_heading level0 row18\" >10</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow18_col0\" class=\"data row18 col0\" >游戏开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow18_col1\" class=\"data row18 col1\" >17812</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow18_col2\" class=\"data row18 col2\" >6500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow18_col3\" class=\"data row18 col3\" >15000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow18_col4\" class=\"data row18 col4\" >44687.500000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow18_col5\" class=\"data row18 col5\" >3480</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow18_col6\" class=\"data row18 col6\" >1.82%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow19_col0\" class=\"data row19 col0\" >iOS开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow19_col1\" class=\"data row19 col1\" >17433</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow19_col2\" class=\"data row19 col2\" >9000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow19_col3\" class=\"data row19 col3\" >16000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow19_col4\" class=\"data row19 col4\" >37428.571429</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow19_col5\" class=\"data row19 col5\" >724</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow19_col6\" class=\"data row19 col6\" >0.38%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row20\" class=\"row_heading level0 row20\" >31</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow20_col0\" class=\"data row20 col0\" >机器人</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow20_col1\" class=\"data row20 col1\" >17159</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow20_col2\" class=\"data row20 col2\" >5283.333333</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow20_col3\" class=\"data row20 col3\" >17500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow20_col4\" class=\"data row20 col4\" >40666.666667</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow20_col5\" class=\"data row20 col5\" >244</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow20_col6\" class=\"data row20 col6\" >0.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row21\" class=\"row_heading level0 row21\" >35</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow21_col0\" class=\"data row21 col0\" >Unity3D</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow21_col1\" class=\"data row21 col1\" >16913</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow21_col2\" class=\"data row21 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow21_col3\" class=\"data row21 col3\" >15000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow21_col4\" class=\"data row21 col4\" >34756.250000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow21_col5\" class=\"data row21 col5\" >979</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow21_col6\" class=\"data row21 col6\" >0.51%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row22\" class=\"row_heading level0 row22\" >16</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow22_col0\" class=\"data row22 col0\" >图像处理工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow22_col1\" class=\"data row22 col1\" >16866</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow22_col2\" class=\"data row22 col2\" >5250.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow22_col3\" class=\"data row22 col3\" >15000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow22_col4\" class=\"data row22 col4\" >35625.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow22_col5\" class=\"data row22 col5\" >290</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow22_col6\" class=\"data row22 col6\" >0.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row23\" class=\"row_heading level0 row23\" >44</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow23_col0\" class=\"data row23 col0\" >CAE</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow23_col1\" class=\"data row23 col1\" >16442</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow23_col2\" class=\"data row23 col2\" >3639.285714</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow23_col3\" class=\"data row23 col3\" >15937</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow23_col4\" class=\"data row23 col4\" >25000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow23_col5\" class=\"data row23 col5\" >86</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow23_col6\" class=\"data row23 col6\" >0.04%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row24\" class=\"row_heading level0 row24\" >6</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow24_col0\" class=\"data row24 col0\" >深度学习工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow24_col1\" class=\"data row24 col1\" >16348</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow24_col2\" class=\"data row24 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow24_col3\" class=\"data row24 col3\" >13580</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow24_col4\" class=\"data row24 col4\" >40000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow24_col5\" class=\"data row24 col5\" >198</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow24_col6\" class=\"data row24 col6\" >0.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row25\" class=\"row_heading level0 row25\" >19</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow25_col0\" class=\"data row25 col0\" >Android开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow25_col1\" class=\"data row25 col1\" >16151</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow25_col2\" class=\"data row25 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow25_col3\" class=\"data row25 col3\" >15000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow25_col4\" class=\"data row25 col4\" >35000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow25_col5\" class=\"data row25 col5\" >1590</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow25_col6\" class=\"data row25 col6\" >0.83%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row26\" class=\"row_heading level0 row26\" >2</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow26_col0\" class=\"data row26 col0\" >软件工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow26_col1\" class=\"data row26 col1\" >15903</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow26_col2\" class=\"data row26 col2\" >6000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow26_col3\" class=\"data row26 col3\" >14500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow26_col4\" class=\"data row26 col4\" >37500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow26_col5\" class=\"data row26 col5\" >121296</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow26_col6\" class=\"data row26 col6\" >63.41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row27\" class=\"row_heading level0 row27\" >40</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow27_col0\" class=\"data row27 col0\" >视觉软件工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow27_col1\" class=\"data row27 col1\" >15711</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow27_col2\" class=\"data row27 col2\" >1750.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow27_col3\" class=\"data row27 col3\" >14833</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow27_col4\" class=\"data row27 col4\" >29370.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow27_col5\" class=\"data row27 col5\" >178</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow27_col6\" class=\"data row27 col6\" >0.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row28\" class=\"row_heading level0 row28\" >7</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow28_col0\" class=\"data row28 col0\" >Web前端开发</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow28_col1\" class=\"data row28 col1\" >15438</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow28_col2\" class=\"data row28 col2\" >6766.666667</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow28_col3\" class=\"data row28 col3\" >14000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow28_col4\" class=\"data row28 col4\" >30000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow28_col5\" class=\"data row28 col5\" >12068</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow28_col6\" class=\"data row28 col6\" >6.31%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row29\" class=\"row_heading level0 row29\" >0</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow29_col0\" class=\"data row29 col0\" >推荐算法工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow29_col1\" class=\"data row29 col1\" >15305</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow29_col2\" class=\"data row29 col2\" >5221.590909</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow29_col3\" class=\"data row29 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow29_col4\" class=\"data row29 col4\" >45000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow29_col5\" class=\"data row29 col5\" >1435</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow29_col6\" class=\"data row29 col6\" >0.75%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row30\" class=\"row_heading level0 row30\" >21</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow30_col0\" class=\"data row30 col0\" >爬虫工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow30_col1\" class=\"data row30 col1\" >15258</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow30_col2\" class=\"data row30 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow30_col3\" class=\"data row30 col3\" >15000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow30_col4\" class=\"data row30 col4\" >27500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow30_col5\" class=\"data row30 col5\" >285</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow30_col6\" class=\"data row30 col6\" >0.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row31\" class=\"row_heading level0 row31\" >18</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow31_col0\" class=\"data row31 col0\" >大数据开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow31_col1\" class=\"data row31 col1\" >14684</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow31_col2\" class=\"data row31 col2\" >5250.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow31_col3\" class=\"data row31 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow31_col4\" class=\"data row31 col4\" >35000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow31_col5\" class=\"data row31 col5\" >2606</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow31_col6\" class=\"data row31 col6\" >1.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row32\" class=\"row_heading level0 row32\" >34</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow32_col0\" class=\"data row32 col0\" >游戏客户端开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow32_col1\" class=\"data row32 col1\" >14679</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow32_col2\" class=\"data row32 col2\" >5410.714286</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow32_col3\" class=\"data row32 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow32_col4\" class=\"data row32 col4\" >30000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow32_col5\" class=\"data row32 col5\" >190</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow32_col6\" class=\"data row32 col6\" >0.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row33\" class=\"row_heading level0 row33\" >27</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow33_col0\" class=\"data row33 col0\" >GIS</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow33_col1\" class=\"data row33 col1\" >14563</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow33_col2\" class=\"data row33 col2\" >6000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow33_col3\" class=\"data row33 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow33_col4\" class=\"data row33 col4\" >25000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow33_col5\" class=\"data row33 col5\" >674</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow33_col6\" class=\"data row33 col6\" >0.35%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row34\" class=\"row_heading level0 row34\" >25</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow34_col0\" class=\"data row34 col0\" >HTML5开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow34_col1\" class=\"data row34 col1\" >14489</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow34_col2\" class=\"data row34 col2\" >6787.500000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow34_col3\" class=\"data row34 col3\" >13000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow34_col4\" class=\"data row34 col4\" >27500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow34_col5\" class=\"data row34 col5\" >489</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow34_col6\" class=\"data row34 col6\" >0.26%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row35\" class=\"row_heading level0 row35\" >24</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow35_col0\" class=\"data row35 col0\" >ETL</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow35_col1\" class=\"data row35 col1\" >14310</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow35_col2\" class=\"data row35 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow35_col3\" class=\"data row35 col3\" >13071</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow35_col4\" class=\"data row35 col4\" >22500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow35_col5\" class=\"data row35 col5\" >739</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow35_col6\" class=\"data row35 col6\" >0.39%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row36\" class=\"row_heading level0 row36\" >1</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow36_col0\" class=\"data row36 col0\" >前端开发</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow36_col1\" class=\"data row36 col1\" >14258</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow36_col2\" class=\"data row36 col2\" >5000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow36_col3\" class=\"data row36 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow36_col4\" class=\"data row36 col4\" >35000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow36_col5\" class=\"data row36 col5\" >4065</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow36_col6\" class=\"data row36 col6\" >2.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row37\" class=\"row_heading level0 row37\" >43</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow37_col0\" class=\"data row37 col0\" >Cocos2d-x开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow37_col1\" class=\"data row37 col1\" >14197</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow37_col2\" class=\"data row37 col2\" >6412.500000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow37_col3\" class=\"data row37 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow37_col4\" class=\"data row37 col4\" >27500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow37_col5\" class=\"data row37 col5\" >71</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow37_col6\" class=\"data row37 col6\" >0.04%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row38\" class=\"row_heading level0 row38\" >4</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow38_col0\" class=\"data row38 col0\" >人工智能</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow38_col1\" class=\"data row38 col1\" >13969</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow38_col2\" class=\"data row38 col2\" >5250.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow38_col3\" class=\"data row38 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow38_col4\" class=\"data row38 col4\" >45000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow38_col5\" class=\"data row38 col5\" >11070</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow38_col6\" class=\"data row38 col6\" >5.79%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row39\" class=\"row_heading level0 row39\" >29</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow39_col0\" class=\"data row39 col0\" >机器视觉工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow39_col1\" class=\"data row39 col1\" >13686</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow39_col2\" class=\"data row39 col2\" >5235.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow39_col3\" class=\"data row39 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow39_col4\" class=\"data row39 col4\" >26916.666667</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow39_col5\" class=\"data row39 col5\" >194</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow39_col6\" class=\"data row39 col6\" >0.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row40\" class=\"row_heading level0 row40\" >38</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow40_col0\" class=\"data row40 col0\" >爬虫开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow40_col1\" class=\"data row40 col1\" >13319</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow40_col2\" class=\"data row40 col2\" >5650.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow40_col3\" class=\"data row40 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow40_col4\" class=\"data row40 col4\" >29550.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow40_col5\" class=\"data row40 col5\" >138</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow40_col6\" class=\"data row40 col6\" >0.07%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row41\" class=\"row_heading level0 row41\" >33</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow41_col0\" class=\"data row41 col0\" >遥感</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow41_col1\" class=\"data row41 col1\" >13303</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow41_col2\" class=\"data row41 col2\" >9000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow41_col3\" class=\"data row41 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow41_col4\" class=\"data row41 col4\" >19444.250000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow41_col5\" class=\"data row41 col5\" >65</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow41_col6\" class=\"data row41 col6\" >0.03%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row42\" class=\"row_heading level0 row42\" >8</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow42_col0\" class=\"data row42 col0\" >搜索算法工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow42_col1\" class=\"data row42 col1\" >12896</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow42_col2\" class=\"data row42 col2\" >5250.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow42_col3\" class=\"data row42 col3\" >10000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow42_col4\" class=\"data row42 col4\" >45000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow42_col5\" class=\"data row42 col5\" >648</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow42_col6\" class=\"data row42 col6\" >0.34%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row43\" class=\"row_heading level0 row43\" >28</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow43_col0\" class=\"data row43 col0\" >系统分析员</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow43_col1\" class=\"data row43 col1\" >12840</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow43_col2\" class=\"data row43 col2\" >3750.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow43_col3\" class=\"data row43 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow43_col4\" class=\"data row43 col4\" >25000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow43_col5\" class=\"data row43 col5\" >485</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow43_col6\" class=\"data row43 col6\" >0.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row44\" class=\"row_heading level0 row44\" >32</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow44_col0\" class=\"data row44 col0\" >游戏服务端开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow44_col1\" class=\"data row44 col1\" >12718</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow44_col2\" class=\"data row44 col2\" >5258.333333</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow44_col3\" class=\"data row44 col3\" >12500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow44_col4\" class=\"data row44 col4\" >30000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow44_col5\" class=\"data row44 col5\" >71</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow44_col6\" class=\"data row44 col6\" >0.04%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow45_col0\" class=\"data row45 col0\" >生物信息</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow45_col1\" class=\"data row45 col1\" >12609</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow45_col2\" class=\"data row45 col2\" >7000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow45_col3\" class=\"data row45 col3\" >14000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow45_col4\" class=\"data row45 col4\" >17387.500000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow45_col5\" class=\"data row45 col5\" >23</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow45_col6\" class=\"data row45 col6\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row46\" class=\"row_heading level0 row46\" >15</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow46_col0\" class=\"data row46 col0\" >小程序开发工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow46_col1\" class=\"data row46 col1\" >11807</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow46_col2\" class=\"data row46 col2\" >5000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow46_col3\" class=\"data row46 col3\" >10500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow46_col4\" class=\"data row46 col4\" >29742.850000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow46_col5\" class=\"data row46 col5\" >257</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow46_col6\" class=\"data row46 col6\" >0.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row47\" class=\"row_heading level0 row47\" >23</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow47_col0\" class=\"data row47 col0\" >系统工程师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow47_col1\" class=\"data row47 col1\" >11556</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow47_col2\" class=\"data row47 col2\" >4200.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow47_col3\" class=\"data row47 col3\" >9500</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow47_col4\" class=\"data row47 col4\" >27500.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow47_col5\" class=\"data row47 col5\" >3038</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow47_col6\" class=\"data row47 col6\" >1.59%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031eflevel0_row48\" class=\"row_heading level0 row48\" >30</th>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow48_col0\" class=\"data row48 col0\" >网站架构设计师</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow48_col1\" class=\"data row48 col1\" >10517</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow48_col2\" class=\"data row48 col2\" >5000.000000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow48_col3\" class=\"data row48 col3\" >7000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow48_col4\" class=\"data row48 col4\" >32515.625000</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow48_col5\" class=\"data row48 col5\" >206</td>\n",
       "                        <td id=\"T_08a7b7b6_7802_11ea_bc94_701ce71031efrow48_col6\" class=\"data row48 col6\" >0.11%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1998b899588>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career = get_sub_stats_by_col(data,'career')\n",
    "data_career.style.format({\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16437.606482812706"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data.monthly_salary * data.headcount) / data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "191275"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    58340.000000\n",
       "mean         3.278625\n",
       "std          5.051038\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          5.000000\n",
       "max        500.000000\n",
       "Name: headcount, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns=100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "191275"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "headcount_by_cities = data.groupby(by='city').headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "上海    64372\n",
       "北京    32729\n",
       "广州    44663\n",
       "深圳    49511\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headcount_by_cities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(headcount_by_cities.values, labels=headcount_by_cities.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_08dbe73e_7802_11ea_b289_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >city</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow0_col0\" class=\"data row0 col0\" >北京</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow0_col1\" class=\"data row0 col1\" >18058</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow0_col2\" class=\"data row0 col2\" >5250</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow0_col3\" class=\"data row0 col3\" >15500</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow0_col4\" class=\"data row0 col4\" >42500</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow0_col5\" class=\"data row0 col5\" >32729</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow0_col6\" class=\"data row0 col6\" >17.11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow1_col0\" class=\"data row1 col0\" >上海</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow1_col1\" class=\"data row1 col1\" >16863</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow1_col2\" class=\"data row1 col2\" >6500</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow1_col3\" class=\"data row1 col3\" >15000</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow1_col4\" class=\"data row1 col4\" >40000</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow1_col5\" class=\"data row1 col5\" >64372</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow1_col6\" class=\"data row1 col6\" >33.65%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow2_col0\" class=\"data row2 col0\" >深圳</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow2_col1\" class=\"data row2 col1\" >16623</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow2_col2\" class=\"data row2 col2\" >6000</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow2_col5\" class=\"data row2 col5\" >49511</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow2_col6\" class=\"data row2 col6\" >25.88%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow3_col0\" class=\"data row3 col0\" >广州</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow3_col1\" class=\"data row3 col1\" >14432</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow3_col2\" class=\"data row3 col2\" >5250</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow3_col4\" class=\"data row3 col4\" >30000</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow3_col5\" class=\"data row3 col5\" >44663</td>\n",
       "                        <td id=\"T_08dbe73e_7802_11ea_b289_701ce71031efrow3_col6\" class=\"data row3 col6\" >23.35%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x19987e64888>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_col(data,'city')\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_beijing=data[data.city=='北京'].monthly_salary\n",
    "salary_shanghai=data[data.city=='上海'].monthly_salary\n",
    "salary_guangzhou=data[data.city=='广州'].monthly_salary\n",
    "salary_shenzhen=data[data.city=='深圳'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=136.33541200192516, pvalue=1.987014554011544e-31)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_beijing, salary_shanghai)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=108.23702744220992, pvalue=2.5924301089284636e-25)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shanghai, salary_shenzhen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=962.2188095896075, pvalue=5.9553797201273e-208)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shenzhen, salary_guangzhou)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_090df39e_7802_11ea_b108_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow0_col1\" class=\"data row0 col1\" >rust</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow0_col2\" class=\"data row0 col2\" >22567</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow0_col3\" class=\"data row0 col3\" >25000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow0_col4\" class=\"data row0 col4\" >4000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow0_col5\" class=\"data row0 col5\" >45000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow0_col6\" class=\"data row0 col6\" >458</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.17%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow1_col1\" class=\"data row1 col1\" >scala</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow1_col2\" class=\"data row1 col2\" >22433</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow1_col3\" class=\"data row1 col3\" >20250</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow1_col4\" class=\"data row1 col4\" >8500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow1_col5\" class=\"data row1 col5\" >50000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow1_col6\" class=\"data row1 col6\" >2288</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.84%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow2_col1\" class=\"data row2 col1\" >ruby</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow2_col2\" class=\"data row2 col2\" >21497</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow2_col3\" class=\"data row2 col3\" >17500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow2_col4\" class=\"data row2 col4\" >4604</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow2_col5\" class=\"data row2 col5\" >45000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow2_col6\" class=\"data row2 col6\" >1769</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow2_col7\" class=\"data row2 col7\" >0.65%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow3_col1\" class=\"data row3 col1\" >matlab</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow3_col2\" class=\"data row3 col2\" >20970</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow3_col3\" class=\"data row3 col3\" >19000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow3_col4\" class=\"data row3 col4\" >7000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow3_col5\" class=\"data row3 col5\" >50000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow3_col6\" class=\"data row3 col6\" >3727</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow3_col7\" class=\"data row3 col7\" >1.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow4_col2\" class=\"data row4 col2\" >20843</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow4_col4\" class=\"data row4 col4\" >7000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow4_col5\" class=\"data row4 col5\" >45000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow4_col6\" class=\"data row4 col6\" >25695</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow4_col7\" class=\"data row4 col7\" >9.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow5_col1\" class=\"data row5 col1\" >go</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow5_col2\" class=\"data row5 col2\" >20205</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow5_col3\" class=\"data row5 col3\" >17500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow5_col4\" class=\"data row5 col4\" >7000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow5_col5\" class=\"data row5 col5\" >45000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow5_col6\" class=\"data row5 col6\" >22696</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow5_col7\" class=\"data row5 col7\" >8.32%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow6_col1\" class=\"data row6 col1\" >lua</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow6_col2\" class=\"data row6 col2\" >19792</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow6_col3\" class=\"data row6 col3\" >17500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow6_col4\" class=\"data row6 col4\" >7000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow6_col5\" class=\"data row6 col5\" >41597</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow6_col6\" class=\"data row6 col6\" >3505</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow6_col7\" class=\"data row6 col7\" >1.28%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow7_col1\" class=\"data row7 col1\" >perl</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow7_col2\" class=\"data row7 col2\" >19440</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow7_col3\" class=\"data row7 col3\" >17500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow7_col4\" class=\"data row7 col4\" >5460</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow7_col5\" class=\"data row7 col5\" >49833</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow7_col6\" class=\"data row7 col6\" >1868</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow8_col1\" class=\"data row8 col1\" >kotlin</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow8_col2\" class=\"data row8 col2\" >19124</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow8_col3\" class=\"data row8 col3\" >17500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow8_col4\" class=\"data row8 col4\" >9000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow8_col5\" class=\"data row8 col5\" >37458</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow8_col6\" class=\"data row8 col6\" >761</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.28%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow9_col1\" class=\"data row9 col1\" >r</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow9_col2\" class=\"data row9 col2\" >19109</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow9_col3\" class=\"data row9 col3\" >17500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow9_col4\" class=\"data row9 col4\" >5250</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow9_col5\" class=\"data row9 col5\" >45000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow9_col6\" class=\"data row9 col6\" >2858</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow9_col7\" class=\"data row9 col7\" >1.05%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow10_col1\" class=\"data row10 col1\" >swift</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow10_col2\" class=\"data row10 col2\" >19028</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow10_col3\" class=\"data row10 col3\" >17500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow10_col4\" class=\"data row10 col4\" >7500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow10_col5\" class=\"data row10 col5\" >37500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow10_col6\" class=\"data row10 col6\" >1865</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow10_col7\" class=\"data row10 col7\" >0.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow11_col1\" class=\"data row11 col1\" >cpp</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow11_col2\" class=\"data row11 col2\" >17522</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow11_col3\" class=\"data row11 col3\" >15000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow11_col4\" class=\"data row11 col4\" >6500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow11_col5\" class=\"data row11 col5\" >40000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow11_col6\" class=\"data row11 col6\" >63318</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow11_col7\" class=\"data row11 col7\" >23.21%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow12_col1\" class=\"data row12 col1\" >java</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow12_col2\" class=\"data row12 col2\" >16849</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow12_col3\" class=\"data row12 col3\" >15000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow12_col4\" class=\"data row12 col4\" >6500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow12_col5\" class=\"data row12 col5\" >37500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow12_col6\" class=\"data row12 col6\" >70892</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow12_col7\" class=\"data row12 col7\" >25.98%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow13_col1\" class=\"data row13 col1\" >haskell</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow13_col2\" class=\"data row13 col2\" >16818</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow13_col3\" class=\"data row13 col3\" >15000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow13_col4\" class=\"data row13 col4\" >11500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow13_col5\" class=\"data row13 col5\" >27000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow13_col6\" class=\"data row13 col6\" >33</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow13_col7\" class=\"data row13 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow14_col1\" class=\"data row14 col1\" >typescript</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow14_col2\" class=\"data row14 col2\" >16786</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow14_col3\" class=\"data row14 col3\" >15000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow14_col4\" class=\"data row14 col4\" >7500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow14_col5\" class=\"data row14 col5\" >35589</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow14_col6\" class=\"data row14 col6\" >2347</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow14_col7\" class=\"data row14 col7\" >0.86%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow15_col1\" class=\"data row15 col1\" >php</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow15_col2\" class=\"data row15 col2\" >16276</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow15_col3\" class=\"data row15 col3\" >12500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow15_col4\" class=\"data row15 col4\" >5250</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow15_col5\" class=\"data row15 col5\" >42500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow15_col6\" class=\"data row15 col6\" >10770</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow15_col7\" class=\"data row15 col7\" >3.95%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow16_col1\" class=\"data row16 col1\" >objective_c</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow16_col2\" class=\"data row16 col2\" >15447</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow16_col3\" class=\"data row16 col3\" >15000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow16_col4\" class=\"data row16 col4\" >7012</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow16_col5\" class=\"data row16 col5\" >26625</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow16_col6\" class=\"data row16 col6\" >161</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow16_col7\" class=\"data row16 col7\" >0.06%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow17_col1\" class=\"data row17 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow17_col2\" class=\"data row17 col2\" >14572</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow17_col3\" class=\"data row17 col3\" >12500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow17_col4\" class=\"data row17 col4\" >5250</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow17_col5\" class=\"data row17 col5\" >35000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow17_col6\" class=\"data row17 col6\" >21409</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow17_col7\" class=\"data row17 col7\" >7.85%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow18_col1\" class=\"data row18 col1\" >javascript</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow18_col2\" class=\"data row18 col2\" >14286</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow18_col3\" class=\"data row18 col3\" >12500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow18_col4\" class=\"data row18 col4\" >6000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow18_col5\" class=\"data row18 col5\" >29166</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow18_col6\" class=\"data row18 col6\" >35605</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow18_col7\" class=\"data row18 col7\" >13.05%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow19_col1\" class=\"data row19 col1\" >julia</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow19_col2\" class=\"data row19 col2\" >14167</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow19_col3\" class=\"data row19 col3\" >14167</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow19_col4\" class=\"data row19 col4\" >12500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow19_col5\" class=\"data row19 col5\" >22500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow19_col6\" class=\"data row19 col6\" >6</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow20_col1\" class=\"data row20 col1\" >delphi</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow20_col2\" class=\"data row20 col2\" >13353</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow20_col3\" class=\"data row20 col3\" >12500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow20_col4\" class=\"data row20 col4\" >5500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow20_col5\" class=\"data row20 col5\" >24000</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow20_col6\" class=\"data row20 col6\" >437</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.16%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow21_col0\" class=\"data row21 col0\" >22</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow21_col1\" class=\"data row21 col1\" >visual_basic</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow21_col2\" class=\"data row21 col2\" >12199</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow21_col3\" class=\"data row21 col3\" >12083</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow21_col4\" class=\"data row21 col4\" >6400</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow21_col5\" class=\"data row21 col5\" >22500</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow21_col6\" class=\"data row21 col6\" >28</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow21_col7\" class=\"data row21 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow22_col0\" class=\"data row22 col0\" >23</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow22_col1\" class=\"data row22 col1\" >vba</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow22_col2\" class=\"data row22 col2\" >11564</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow22_col3\" class=\"data row22 col3\" >11286</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow22_col4\" class=\"data row22 col4\" >5250</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow22_col5\" class=\"data row22 col5\" >22612</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow22_col6\" class=\"data row22 col6\" >334</td>\n",
       "                        <td id=\"T_090df39e_7802_11ea_b108_701ce71031efrow22_col7\" class=\"data row22 col7\" >0.12%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1998a25a908>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据显示，haskell才是最赚钱的编程语言。python是主流语言里面最赚钱的，比java的工资多了3000元！vb是最不赚钱的了。其中，最赚钱的编程语言和最不赚钱的，工资居然差了2倍。所以，要选好编程语言呀！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_counts=data[~(data.edu=='')].groupby(by=['edu']).headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu\n",
       "初中及以下        89\n",
       "博士          834\n",
       "大专        52821\n",
       "本科       111288\n",
       "硕士         6426\n",
       "高中          890\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(edu_counts.values, labels=edu_counts.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_09507ad0_7802_11ea_bc65_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >edu</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow0_col0\" class=\"data row0 col0\" >博士</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow0_col1\" class=\"data row0 col1\" >25557</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow0_col2\" class=\"data row0 col2\" >1925</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow0_col3\" class=\"data row0 col3\" >25000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow0_col5\" class=\"data row0 col5\" >834</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.48%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow1_col0\" class=\"data row1 col0\" >硕士</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow1_col1\" class=\"data row1 col1\" >21533</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow1_col2\" class=\"data row1 col2\" >8000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow1_col5\" class=\"data row1 col5\" >6426</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow1_col6\" class=\"data row1 col6\" >3.73%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow2_col0\" class=\"data row2 col0\" >本科</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow2_col1\" class=\"data row2 col1\" >17384</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow2_col2\" class=\"data row2 col2\" >6666</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow2_col4\" class=\"data row2 col4\" >40000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow2_col5\" class=\"data row2 col5\" >111288</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow2_col6\" class=\"data row2 col6\" >64.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow3_col0\" class=\"data row3 col0\" >大专</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow3_col1\" class=\"data row3 col1\" >14184</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow3_col2\" class=\"data row3 col2\" >5250</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow3_col4\" class=\"data row3 col4\" >27500</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow3_col5\" class=\"data row3 col5\" >52821</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow3_col6\" class=\"data row3 col6\" >30.65%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow4_col0\" class=\"data row4 col0\" >高中</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow4_col1\" class=\"data row4 col1\" >10375</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow4_col2\" class=\"data row4 col2\" >4000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow4_col3\" class=\"data row4 col3\" >9000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow4_col4\" class=\"data row4 col4\" >35000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow4_col5\" class=\"data row4 col5\" >890</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.52%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow5_col0\" class=\"data row5 col0\" >初中及以下</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow5_col1\" class=\"data row5 col1\" >7610</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow5_col2\" class=\"data row5 col2\" >3750</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow5_col3\" class=\"data row5 col3\" >7000</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow5_col4\" class=\"data row5 col4\" >18875</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow5_col5\" class=\"data row5 col5\" >89</td>\n",
       "                        <td id=\"T_09507ad0_7802_11ea_bc65_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.05%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1998f370908>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats_by_col(data[data.edu.isin(['初中及以下','高中','大专','本科','硕士','博士'])], 'edu')\n",
    "apply_style(data_edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_associate=data[data.edu=='大专'].monthly_salary\n",
    "salary_bachelor=data[data.edu=='本科'].monthly_salary\n",
    "salary_master=data[data.edu=='硕士'].monthly_salary\n",
    "salary_phd=data[data.edu=='博士'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是13339，17463，22070，26975。中位数分别是12500， 15499，20000， 25000。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=46.767711989474115, pvalue=1.0194242794280853e-11)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=614.4596430531374, pvalue=1.4510494190432017e-134)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=3167.936712046677, pvalue=0.0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['大专','本科','硕士','博士']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_09a99892_7802_11ea_8d29_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >experience</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow0_col0\" class=\"data row0 col0\" >10+</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow0_col1\" class=\"data row0 col1\" >31911</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow0_col2\" class=\"data row0 col2\" >12500</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow0_col3\" class=\"data row0 col3\" >30000</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow0_col4\" class=\"data row0 col4\" >60000</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow0_col5\" class=\"data row0 col5\" >675</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.35%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow1_col1\" class=\"data row1 col1\" >23101</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow1_col2\" class=\"data row1 col2\" >11500</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow1_col3\" class=\"data row1 col3\" >20500</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow1_col5\" class=\"data row1 col5\" >24928</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow1_col6\" class=\"data row1 col6\" >13.03%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow2_col1\" class=\"data row2 col1\" >18060</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow2_col2\" class=\"data row2 col2\" >9000</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow2_col3\" class=\"data row2 col3\" >16500</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow2_col5\" class=\"data row2 col5\" >61571</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow2_col6\" class=\"data row2 col6\" >32.19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow3_col0\" class=\"data row3 col0\" ></td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow3_col1\" class=\"data row3 col1\" >14052</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow3_col2\" class=\"data row3 col2\" >4750</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow3_col4\" class=\"data row3 col4\" >35000</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow3_col5\" class=\"data row3 col5\" >50801</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow3_col6\" class=\"data row3 col6\" >26.56%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow4_col0\" class=\"data row4 col0\" >1_3</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow4_col1\" class=\"data row4 col1\" >13525</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow4_col2\" class=\"data row4 col2\" >5500</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow4_col3\" class=\"data row4 col3\" >12500</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow4_col4\" class=\"data row4 col4\" >30000</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow4_col5\" class=\"data row4 col5\" >53300</td>\n",
       "                        <td id=\"T_09a99892_7802_11ea_8d29_701ce71031efrow4_col6\" class=\"data row4 col6\" >27.87%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x199909dbcc8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats_by_col(data, 'experience')\n",
    "apply_style(data_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience=='10+'].monthly_salary\n",
    "salary_we_5_10=data[data.experience=='5_10'].monthly_salary\n",
    "salary_we_3_5=data[data.experience=='3_5'].monthly_salary\n",
    "salary_we_1_3=data[data.experience=='1_3'].monthly_salary\n",
    "salary_we_no=data[data.experience=='no'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['10+','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'company_type'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2646\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'company_type'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-47-8570df4a4801>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata_company_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mget_sub_stats_by_col\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'company_type'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mapply_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_company_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-18-d1d14471b9f6>\u001b[0m in \u001b[0;36mget_sub_stats_by_col\u001b[1;34m(data, col)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#Common Functions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_sub_stats_by_col\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mcategories\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0msalary_mean\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0msalary_95_min\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2798\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2799\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2800\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2801\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2646\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2648\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'company_type'"
     ]
    }
   ],
   "source": [
    "data_company_type=get_sub_stats_by_col(data,'company_type')\n",
    "apply_style(data_company_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_company_size=get_sub_stats_by_col(data,'company_size')\n",
    "apply_style(data_company_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_industry=get_sub_stats_by_col(data,'industry')\n",
    "apply_style(data_industry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
